Where I'm at
Eighteen days in. The system runs itself now — that sentence has become the new normal, which is wild when I think about where I was on Day 1. Briefings fire, content drafts, X mirrors. The machine hums.
But something was bugging me. Not a bug — a question. Every time I start a conversation with my agent, it loads a bunch of files into memory. Personality files, rules, notes, past learnings. All of it gets read before the AI even sees my first message.
That's context. And context costs tokens. And tokens cost money.
I'd been tracking euros on the dashboard since Day 11. But I'd never once asked: how many tokens is the system actually burning just to boot up? What's it loading? And does any of it matter?
Today I asked. The answer was worse than I expected.
• • •I finally looked at what the system was actually carrying. Most of it was dead weight.
What's a token, and why it matters
Quick detour for anyone following along from the beginning. When you talk to an AI, everything gets converted into tokens — small chunks of text, roughly one token per word. Every token you send costs money. Every token the AI reads takes up space in its "attention window" — the amount of text it can think about at once.
Load too much into that window and two things happen: the bill goes up, and the AI gets distracted. It's like handing someone a 200-page briefing document before asking them a simple question. They'll read all of it. Most of it won't help. And you're paying by the page.
So the question isn't just "how much does this cost?" It's "how much of what I'm loading is actually useful?"
• • •The dead weight
I built a token dashboard — a simple page at /token-dashboard that shows exactly what gets loaded into the AI's context every session. The numbers told the story.
The core files — personality, rules, agent instructions, user profile — come in at about 4,200 tokens. That's the essential stuff. The AI needs it to know who I am, how I want things done, and what roles it plays. Fine. Worth every token.
Then I looked at the memory files. The AI's long-term memory — everything it's learned about me, my patterns, my preferences, accumulated over eighteen days of conversations. About 32,500 tokens. That felt high, but at least it's functional. The AI uses this to avoid repeating mistakes and to remember context between sessions.
Then I looked at the workspace. The actual files sitting on the server where the agent operates. And that's where it got ugly.
A file called knowledge-index.md — 140KB. Sitting in the workspace doing nothing. Never referenced by any cron job, never loaded intentionally. But if the AI ever decided to "check the workspace" as part of a task, it could accidentally pull this file into context. That's roughly 35,000 tokens of noise, loaded for no reason, billed at whatever model I'm running.
An insights file from the AI's nightly self-reflection — 27KB, loaded at startup because the bootstrap instructions say "read all memory files." That instruction made sense when the file was small. Now it's bloated with eighteen days of observations, and the AI reads the whole thing before I've even said good morning.
Old backup tarballs from February. HTML dumps from early site versions. Bookmark digests I'd already processed into guides. All sitting in the workspace like boxes in a garage you moved into three weeks ago and haven't unpacked.
Total workspace: 207MB. Not all of it loads into context — but any of it could, depending on what I ask the AI to do. The risk isn't the cost of loading everything. The risk is accidentally loading the wrong thing and blowing a session's budget on a file that shouldn't be there.
• • •Total workspace: 207MB. Not all of it loads into context.
The cleanup
I moved 14MB of dead weight into an archives folder. The old tarballs. The knowledge index. The February dumps. The stale digests. All still there if I ever need them — just not in the AI's line of sight anymore.
The system didn't change. Nothing broke. I just stopped leaving heavy objects where someone could trip over them.
Potential savings: about 63,000 tokens per session in reduced risk. Not a guarantee — more like removing the loaded gun from the kitchen counter. It probably wasn't going to go off. But why have it there?
• • •Meanwhile, the world burned
While I was counting tokens and archiving old files, the Middle East was escalating. The US and Iran moved closer to open conflict. Oil spiked 6% in a day. Bitcoin surged 7% to $73K — people treating it as a safe haven, the way they used to treat gold. Tesla was facing an NHTSA deadline on March 9 over robotaxi incident reviews.
The briefings caught all of it. The morning summary had the oil spike. The evening briefing had the Bitcoin move and the Tesla deadline. The breaking news scanner — the one I built on Day 14 after missing the Dubai drone strike — picked up the Iran escalation from its geopolitical query angle.
I didn't have to scroll X. I didn't have to check news apps. I opened Telegram and everything was there, organized, timestamped, waiting.
That's the system doing its job. Eighteen days ago I was manually scrolling for market news every morning. Now the AI finds it, summarizes it, and delivers it before I wake up.
The cleanup today makes sure that system keeps running efficiently — not just running, but running lean.
• • •Eighteen days ago I was manually scrolling for market news every morning. Now the AI finds it.
What I learned
One thing that connects everything today. Context is a budget. Not just financially — though it is that — but cognitively. Every file you load into an AI's context competes for attention with every other file. Loading more isn't always better. Sometimes it's just more noise.
The AI reads everything you give it, and if half of what you give it is irrelevant, the other half gets less attention.
This is true for AI and it's true for people. The cleaner the input, the better the output. The trick isn't having more information. It's having the right information and nothing else.
The dashboard changed how I think about my system. Before today, the workspace was a black box — stuff went in, I never checked what accumulated. Now I can see it. And the first thing visibility gave me was the realization that half of what was in there shouldn't have been.
• • •End of day
Day 18 complete. Token dashboard built. Fourteen megabytes archived. The system is lighter. The context is cleaner. And for the first time, I can actually see what the AI carries into every conversation.
The world moved today — oil, crypto, geopolitics. The briefings caught it. The system ran. And underneath, the plumbing got a little less clogged.
Not the most dramatic day. But the kind that keeps the dramatic days possible.
The world moved today. The system ran. And underneath, the plumbing got a little less clogged.
Day 18 of ∞ — @astergod Building in public. Learning in public.